stock-market-prediction.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "f6d46147",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:16:18.138956Z",
     "iopub.status.busy": "2024-07-28T04:16:18.138568Z",
     "iopub.status.idle": "2024-07-28T04:16:18.978990Z",
     "shell.execute_reply": "2024-07-28T04:16:18.977922Z"
    },
    "papermill": {
     "duration": 0.854101,
     "end_time": "2024-07-28T04:16:18.981670",
     "exception": false,
     "start_time": "2024-07-28T04:16:18.127569",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pandas as pd\n",
    "import numpy as np\n",
    "df=pd.read_csv('/kaggle/input/5year-asian-paint/ASIANPAINT.NS (1).csv')\n",
    "df = df.dropna(subset=['Adj Close'])\n",
    "# df['New_Price'] = (df.splitFactor).replace(np.inf, 1).cumprod() * df.close\n",
    "# df['close'] = df['New_Price'] / 20\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "4ea2c7ce",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:16:19.001999Z",
     "iopub.status.busy": "2024-07-28T04:16:19.001613Z",
     "iopub.status.idle": "2024-07-28T04:16:19.022174Z",
     "shell.execute_reply": "2024-07-28T04:16:19.020952Z"
    },
    "papermill": {
     "duration": 0.033311,
     "end_time": "2024-07-28T04:16:19.024539",
     "exception": false,
     "start_time": "2024-07-28T04:16:18.991228",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>Date</th>\n",
       "      <th>Open</th>\n",
       "      <th>High</th>\n",
       "      <th>Low</th>\n",
       "      <th>Close</th>\n",
       "      <th>Adj Close</th>\n",
       "      <th>Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>1231</th>\n",
       "      <td>2024-06-05</td>\n",
       "      <td>2864.500000</td>\n",
       "      <td>3027.300049</td>\n",
       "      <td>2864.500000</td>\n",
       "      <td>2961.750000</td>\n",
       "      <td>2933.368164</td>\n",
       "      <td>2410576.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1232</th>\n",
       "      <td>2024-06-06</td>\n",
       "      <td>2960.000000</td>\n",
       "      <td>2961.750000</td>\n",
       "      <td>2882.250000</td>\n",
       "      <td>2904.800049</td>\n",
       "      <td>2876.963867</td>\n",
       "      <td>1689408.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1233</th>\n",
       "      <td>2024-06-07</td>\n",
       "      <td>2904.800049</td>\n",
       "      <td>2943.100098</td>\n",
       "      <td>2888.850098</td>\n",
       "      <td>2927.699951</td>\n",
       "      <td>2899.644287</td>\n",
       "      <td>1600180.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1234</th>\n",
       "      <td>2024-06-10</td>\n",
       "      <td>2928.000000</td>\n",
       "      <td>2951.899902</td>\n",
       "      <td>2915.000000</td>\n",
       "      <td>2937.550049</td>\n",
       "      <td>2909.400146</td>\n",
       "      <td>876867.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1236</th>\n",
       "      <td>2024-06-12</td>\n",
       "      <td>2909.000000</td>\n",
       "      <td>2918.000000</td>\n",
       "      <td>2865.649902</td>\n",
       "      <td>2905.800049</td>\n",
       "      <td>2905.800049</td>\n",
       "      <td>1701408.0</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "            Date         Open         High          Low        Close  \\\n",
       "1231  2024-06-05  2864.500000  3027.300049  2864.500000  2961.750000   \n",
       "1232  2024-06-06  2960.000000  2961.750000  2882.250000  2904.800049   \n",
       "1233  2024-06-07  2904.800049  2943.100098  2888.850098  2927.699951   \n",
       "1234  2024-06-10  2928.000000  2951.899902  2915.000000  2937.550049   \n",
       "1236  2024-06-12  2909.000000  2918.000000  2865.649902  2905.800049   \n",
       "\n",
       "        Adj Close     Volume  \n",
       "1231  2933.368164  2410576.0  \n",
       "1232  2876.963867  1689408.0  \n",
       "1233  2899.644287  1600180.0  \n",
       "1234  2909.400146   876867.0  \n",
       "1236  2905.800049  1701408.0  "
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "37ae0110",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:16:19.045659Z",
     "iopub.status.busy": "2024-07-28T04:16:19.044723Z",
     "iopub.status.idle": "2024-07-28T04:16:19.050897Z",
     "shell.execute_reply": "2024-07-28T04:16:19.049809Z"
    },
    "papermill": {
     "duration": 0.018887,
     "end_time": "2024-07-28T04:16:19.052972",
     "exception": false,
     "start_time": "2024-07-28T04:16:19.034085",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df1=df.reset_index()['Adj Close']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "79591913",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:16:19.074060Z",
     "iopub.status.busy": "2024-07-28T04:16:19.073226Z",
     "iopub.status.idle": "2024-07-28T04:16:19.080968Z",
     "shell.execute_reply": "2024-07-28T04:16:19.079973Z"
    },
    "papermill": {
     "duration": 0.020456,
     "end_time": "2024-07-28T04:16:19.083046",
     "exception": false,
     "start_time": "2024-07-28T04:16:19.062590",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1231    2933.368164\n",
       "1232    2876.963867\n",
       "1233    2899.644287\n",
       "1234    2909.400146\n",
       "1235    2905.800049\n",
       "Name: Adj Close, dtype: float64"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.tail()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a6bfd45e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:16:19.104983Z",
     "iopub.status.busy": "2024-07-28T04:16:19.104041Z",
     "iopub.status.idle": "2024-07-28T04:16:19.366607Z",
     "shell.execute_reply": "2024-07-28T04:16:19.365275Z"
    },
    "papermill": {
     "duration": 0.276597,
     "end_time": "2024-07-28T04:16:19.369228",
     "exception": false,
     "start_time": "2024-07-28T04:16:19.092631",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7d5de2128b80>]"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "plt.plot(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2ba6ed9b",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:16:19.392480Z",
     "iopub.status.busy": "2024-07-28T04:16:19.391359Z",
     "iopub.status.idle": "2024-07-28T04:16:20.523922Z",
     "shell.execute_reply": "2024-07-28T04:16:20.522812Z"
    },
    "papermill": {
     "duration": 1.146755,
     "end_time": "2024-07-28T04:16:20.526545",
     "exception": false,
     "start_time": "2024-07-28T04:16:19.379790",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.preprocessing import MinMaxScaler\n",
    "scaler=MinMaxScaler(feature_range=(0,1))\n",
    "df1=scaler.fit_transform(np.array(df1).reshape(-1,1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "5bf384c3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:16:20.549533Z",
     "iopub.status.busy": "2024-07-28T04:16:20.548644Z",
     "iopub.status.idle": "2024-07-28T04:16:20.555120Z",
     "shell.execute_reply": "2024-07-28T04:16:20.554045Z"
    },
    "papermill": {
     "duration": 0.020624,
     "end_time": "2024-07-28T04:16:20.557591",
     "exception": false,
     "start_time": "2024-07-28T04:16:20.536967",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1236, 1)"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "df1.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5d529263",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:16:20.580548Z",
     "iopub.status.busy": "2024-07-28T04:16:20.579802Z",
     "iopub.status.idle": "2024-07-28T04:16:20.585163Z",
     "shell.execute_reply": "2024-07-28T04:16:20.584103Z"
    },
    "papermill": {
     "duration": 0.019007,
     "end_time": "2024-07-28T04:16:20.587293",
     "exception": false,
     "start_time": "2024-07-28T04:16:20.568286",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "training_size=int(len(df1)*0.7)\n",
    "test_size=len(df1)-training_size\n",
    "train_data,test_data=df1[0:training_size,:],df1[training_size:len(df1),:1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "ecbda7a0",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:16:20.610250Z",
     "iopub.status.busy": "2024-07-28T04:16:20.609302Z",
     "iopub.status.idle": "2024-07-28T04:16:20.615875Z",
     "shell.execute_reply": "2024-07-28T04:16:20.614737Z"
    },
    "papermill": {
     "duration": 0.020314,
     "end_time": "2024-07-28T04:16:20.617980",
     "exception": false,
     "start_time": "2024-07-28T04:16:20.597666",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(865, 371)"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "training_size,test_size"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "3d930e26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:16:20.640836Z",
     "iopub.status.busy": "2024-07-28T04:16:20.639885Z",
     "iopub.status.idle": "2024-07-28T04:16:20.646776Z",
     "shell.execute_reply": "2024-07-28T04:16:20.645676Z"
    },
    "papermill": {
     "duration": 0.020478,
     "end_time": "2024-07-28T04:16:20.648962",
     "exception": false,
     "start_time": "2024-07-28T04:16:20.628484",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0.04165054],\n",
       "       [0.0466632 ],\n",
       "       [0.03947776],\n",
       "       [0.03483068],\n",
       "       [0.02540771],\n",
       "       [0.02588099],\n",
       "       [0.03057097],\n",
       "       [0.02667694],\n",
       "       [0.02372968],\n",
       "       [0.01811457]])"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "train_data[:10]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ba008253",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:16:20.671651Z",
     "iopub.status.busy": "2024-07-28T04:16:20.670975Z",
     "iopub.status.idle": "2024-07-28T04:16:20.677004Z",
     "shell.execute_reply": "2024-07-28T04:16:20.675978Z"
    },
    "papermill": {
     "duration": 0.01973,
     "end_time": "2024-07-28T04:16:20.679059",
     "exception": false,
     "start_time": "2024-07-28T04:16:20.659329",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "def create_dataset(dataset, time_step=1):\n",
    "\tdataX, dataY = [], []\n",
    "\tfor i in range(len(dataset)-time_step-1):\n",
    "\t\ta = dataset[i:(i+time_step), 0]\n",
    "\t\tdataX.append(a)\n",
    "\t\tdataY.append(dataset[i + time_step, 0])\n",
    "\treturn np.array(dataX), np.array(dataY)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "8ae71482",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:16:20.701909Z",
     "iopub.status.busy": "2024-07-28T04:16:20.701481Z",
     "iopub.status.idle": "2024-07-28T04:16:20.710332Z",
     "shell.execute_reply": "2024-07-28T04:16:20.709148Z"
    },
    "papermill": {
     "duration": 0.02309,
     "end_time": "2024-07-28T04:16:20.712675",
     "exception": false,
     "start_time": "2024-07-28T04:16:20.689585",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "time_step = 100\n",
    "X_train, y_train = create_dataset(train_data, time_step)\n",
    "X_test, y_test = create_dataset(test_data, time_step)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "3fb81d26",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:16:20.735867Z",
     "iopub.status.busy": "2024-07-28T04:16:20.735450Z",
     "iopub.status.idle": "2024-07-28T04:16:20.740934Z",
     "shell.execute_reply": "2024-07-28T04:16:20.739945Z"
    },
    "papermill": {
     "duration": 0.019854,
     "end_time": "2024-07-28T04:16:20.743361",
     "exception": false,
     "start_time": "2024-07-28T04:16:20.723507",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(764, 100)\n",
      "(270,)\n"
     ]
    }
   ],
   "source": [
    "print(X_train.shape)\n",
    "print(y_test.shape)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "2be88e7e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:16:20.766535Z",
     "iopub.status.busy": "2024-07-28T04:16:20.766120Z",
     "iopub.status.idle": "2024-07-28T04:16:20.771287Z",
     "shell.execute_reply": "2024-07-28T04:16:20.770331Z"
    },
    "papermill": {
     "duration": 0.019129,
     "end_time": "2024-07-28T04:16:20.773418",
     "exception": false,
     "start_time": "2024-07-28T04:16:20.754289",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# need 3d model for stacked lstm\n",
    "X_train =X_train.reshape(X_train.shape[0],X_train.shape[1] , 1)\n",
    "X_test = X_test.reshape(X_test.shape[0],X_test.shape[1] , 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "cf3e7a83",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:16:20.796141Z",
     "iopub.status.busy": "2024-07-28T04:16:20.795747Z",
     "iopub.status.idle": "2024-07-28T04:16:33.981592Z",
     "shell.execute_reply": "2024-07-28T04:16:33.980368Z"
    },
    "papermill": {
     "duration": 13.200169,
     "end_time": "2024-07-28T04:16:33.984267",
     "exception": false,
     "start_time": "2024-07-28T04:16:20.784098",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "2024-07-28 04:16:23.088245: E external/local_xla/xla/stream_executor/cuda/cuda_dnn.cc:9261] Unable to register cuDNN factory: Attempting to register factory for plugin cuDNN when one has already been registered\n",
      "2024-07-28 04:16:23.088417: E external/local_xla/xla/stream_executor/cuda/cuda_fft.cc:607] Unable to register cuFFT factory: Attempting to register factory for plugin cuFFT when one has already been registered\n",
      "2024-07-28 04:16:23.243419: E external/local_xla/xla/stream_executor/cuda/cuda_blas.cc:1515] Unable to register cuBLAS factory: Attempting to register factory for plugin cuBLAS when one has already been registered\n"
     ]
    }
   ],
   "source": [
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense, Input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "0798f495",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:16:34.018594Z",
     "iopub.status.busy": "2024-07-28T04:16:34.017939Z",
     "iopub.status.idle": "2024-07-28T04:16:34.188816Z",
     "shell.execute_reply": "2024-07-28T04:16:34.187703Z"
    },
    "papermill": {
     "duration": 0.18856,
     "end_time": "2024-07-28T04:16:34.191175",
     "exception": false,
     "start_time": "2024-07-28T04:16:34.002615",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/opt/conda/lib/python3.10/site-packages/keras/src/layers/rnn/rnn.py:204: UserWarning: Do not pass an `input_shape`/`input_dim` argument to a layer. When using Sequential models, prefer using an `Input(shape)` object as the first layer in the model instead.\n",
      "  super().__init__(**kwargs)\n"
     ]
    }
   ],
   "source": [
    "model = Sequential()\n",
    "model.add(LSTM(50, return_sequences=True, input_shape=(100, 1)))\n",
    "model.add(LSTM(50, return_sequences=True))\n",
    "model.add(LSTM(50))\n",
    "model.add(Dense(1))\n",
    "model.compile(optimizer='adam', loss='mean_squared_error', metrics=['accuracy'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "b0c50216",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:16:34.215216Z",
     "iopub.status.busy": "2024-07-28T04:16:34.214423Z",
     "iopub.status.idle": "2024-07-28T04:16:34.236236Z",
     "shell.execute_reply": "2024-07-28T04:16:34.235249Z"
    },
    "papermill": {
     "duration": 0.036292,
     "end_time": "2024-07-28T04:16:34.238441",
     "exception": false,
     "start_time": "2024-07-28T04:16:34.202149",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"sequential\"</span>\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1mModel: \"sequential\"\u001b[0m\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃<span style=\"font-weight: bold\"> Layer (type)                    </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">       Param # </span>┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                     │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">10,400</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">100</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)        │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">LSTM</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">50</span>)             │        <span style=\"color: #00af00; text-decoration-color: #00af00\">20,200</span> │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dense</span>)                   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">1</span>)              │            <span style=\"color: #00af00; text-decoration-color: #00af00\">51</span> │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n",
       "</pre>\n"
      ],
      "text/plain": [
       "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━┓\n",
       "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)                   \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m      Param #\u001b[0m\u001b[1m \u001b[0m┃\n",
       "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━┩\n",
       "│ lstm (\u001b[38;5;33mLSTM\u001b[0m)                     │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m50\u001b[0m)        │        \u001b[38;5;34m10,400\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_1 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m100\u001b[0m, \u001b[38;5;34m50\u001b[0m)        │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ lstm_2 (\u001b[38;5;33mLSTM\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m50\u001b[0m)             │        \u001b[38;5;34m20,200\u001b[0m │\n",
       "├─────────────────────────────────┼────────────────────────┼───────────────┤\n",
       "│ dense (\u001b[38;5;33mDense\u001b[0m)                   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m1\u001b[0m)              │            \u001b[38;5;34m51\u001b[0m │\n",
       "└─────────────────────────────────┴────────────────────────┴───────────────┘\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">50,851</span> (198.64 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m50,851\u001b[0m (198.64 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">50,851</span> (198.64 KB)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m50,851\u001b[0m (198.64 KB)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
       "</pre>\n"
      ],
      "text/plain": [
       "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "431a20e5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:16:34.263191Z",
     "iopub.status.busy": "2024-07-28T04:16:34.262808Z",
     "iopub.status.idle": "2024-07-28T04:19:26.242453Z",
     "shell.execute_reply": "2024-07-28T04:19:26.241346Z"
    },
    "papermill": {
     "duration": 171.994852,
     "end_time": "2024-07-28T04:19:26.244818",
     "exception": false,
     "start_time": "2024-07-28T04:16:34.249966",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m7s\u001b[0m 184ms/step - accuracy: 0.0000e+00 - loss: 0.2296 - val_accuracy: 0.0037 - val_loss: 0.0029\n",
      "Epoch 2/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 136ms/step - accuracy: 0.0000e+00 - loss: 0.0153 - val_accuracy: 0.0037 - val_loss: 0.0044\n",
      "Epoch 3/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 137ms/step - accuracy: 0.0000e+00 - loss: 0.0101 - val_accuracy: 0.0037 - val_loss: 0.0098\n",
      "Epoch 4/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0058 - val_accuracy: 0.0037 - val_loss: 0.0022\n",
      "Epoch 5/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 136ms/step - accuracy: 0.0000e+00 - loss: 0.0037 - val_accuracy: 0.0037 - val_loss: 0.0031\n",
      "Epoch 6/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 136ms/step - accuracy: 0.0000e+00 - loss: 0.0033 - val_accuracy: 0.0037 - val_loss: 0.0022\n",
      "Epoch 7/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 135ms/step - accuracy: 0.0000e+00 - loss: 0.0030 - val_accuracy: 0.0037 - val_loss: 0.0021\n",
      "Epoch 8/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 135ms/step - accuracy: 0.0000e+00 - loss: 0.0030 - val_accuracy: 0.0037 - val_loss: 0.0021\n",
      "Epoch 9/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 142ms/step - accuracy: 0.0000e+00 - loss: 0.0029 - val_accuracy: 0.0037 - val_loss: 0.0021\n",
      "Epoch 10/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 141ms/step - accuracy: 0.0000e+00 - loss: 0.0030 - val_accuracy: 0.0037 - val_loss: 0.0022\n",
      "Epoch 11/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0030 - val_accuracy: 0.0037 - val_loss: 0.0020\n",
      "Epoch 12/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0029 - val_accuracy: 0.0037 - val_loss: 0.0020\n",
      "Epoch 13/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0029 - val_accuracy: 0.0037 - val_loss: 0.0019\n",
      "Epoch 14/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0027 - val_accuracy: 0.0037 - val_loss: 0.0019\n",
      "Epoch 15/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 136ms/step - accuracy: 0.0000e+00 - loss: 0.0027 - val_accuracy: 0.0037 - val_loss: 0.0019\n",
      "Epoch 16/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0028 - val_accuracy: 0.0037 - val_loss: 0.0018\n",
      "Epoch 17/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0026 - val_accuracy: 0.0037 - val_loss: 0.0017\n",
      "Epoch 18/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0027 - val_accuracy: 0.0037 - val_loss: 0.0017\n",
      "Epoch 19/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0024 - val_accuracy: 0.0037 - val_loss: 0.0016\n",
      "Epoch 20/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0026 - val_accuracy: 0.0037 - val_loss: 0.0016\n",
      "Epoch 21/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 135ms/step - accuracy: 0.0000e+00 - loss: 0.0023 - val_accuracy: 0.0037 - val_loss: 0.0016\n",
      "Epoch 22/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0023 - val_accuracy: 0.0037 - val_loss: 0.0014\n",
      "Epoch 23/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0023 - val_accuracy: 0.0037 - val_loss: 0.0014\n",
      "Epoch 24/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0020 - val_accuracy: 0.0037 - val_loss: 0.0014\n",
      "Epoch 25/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 132ms/step - accuracy: 0.0000e+00 - loss: 0.0021 - val_accuracy: 0.0037 - val_loss: 0.0013\n",
      "Epoch 26/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0024 - val_accuracy: 0.0037 - val_loss: 0.0016\n",
      "Epoch 27/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 135ms/step - accuracy: 0.0000e+00 - loss: 0.0021 - val_accuracy: 0.0037 - val_loss: 0.0012\n",
      "Epoch 28/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 141ms/step - accuracy: 0.0000e+00 - loss: 0.0020 - val_accuracy: 0.0037 - val_loss: 0.0015\n",
      "Epoch 29/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 137ms/step - accuracy: 0.0000e+00 - loss: 0.0020 - val_accuracy: 0.0037 - val_loss: 0.0014\n",
      "Epoch 30/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0020 - val_accuracy: 0.0037 - val_loss: 0.0017\n",
      "Epoch 31/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0023 - val_accuracy: 0.0037 - val_loss: 0.0012\n",
      "Epoch 32/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0019 - val_accuracy: 0.0037 - val_loss: 0.0011\n",
      "Epoch 33/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 135ms/step - accuracy: 0.0000e+00 - loss: 0.0019 - val_accuracy: 0.0037 - val_loss: 0.0012\n",
      "Epoch 34/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 132ms/step - accuracy: 0.0000e+00 - loss: 0.0021 - val_accuracy: 0.0037 - val_loss: 0.0011\n",
      "Epoch 35/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 132ms/step - accuracy: 0.0000e+00 - loss: 0.0018 - val_accuracy: 0.0037 - val_loss: 0.0011\n",
      "Epoch 36/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0017 - val_accuracy: 0.0037 - val_loss: 0.0011\n",
      "Epoch 37/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 132ms/step - accuracy: 0.0000e+00 - loss: 0.0017 - val_accuracy: 0.0037 - val_loss: 0.0012\n",
      "Epoch 38/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0020 - val_accuracy: 0.0037 - val_loss: 0.0013\n",
      "Epoch 39/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 136ms/step - accuracy: 0.0000e+00 - loss: 0.0018 - val_accuracy: 0.0037 - val_loss: 0.0012\n",
      "Epoch 40/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0019 - val_accuracy: 0.0037 - val_loss: 0.0012\n",
      "Epoch 41/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0018 - val_accuracy: 0.0037 - val_loss: 0.0011\n",
      "Epoch 42/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 132ms/step - accuracy: 0.0000e+00 - loss: 0.0017 - val_accuracy: 0.0037 - val_loss: 0.0010\n",
      "Epoch 43/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 132ms/step - accuracy: 0.0000e+00 - loss: 0.0017 - val_accuracy: 0.0037 - val_loss: 0.0011\n",
      "Epoch 44/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0019 - val_accuracy: 0.0037 - val_loss: 9.9198e-04\n",
      "Epoch 45/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 136ms/step - accuracy: 0.0000e+00 - loss: 0.0017 - val_accuracy: 0.0037 - val_loss: 9.7459e-04\n",
      "Epoch 46/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0016 - val_accuracy: 0.0037 - val_loss: 9.5826e-04\n",
      "Epoch 47/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 135ms/step - accuracy: 0.0000e+00 - loss: 0.0016 - val_accuracy: 0.0037 - val_loss: 0.0017\n",
      "Epoch 48/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 144ms/step - accuracy: 0.0000e+00 - loss: 0.0018 - val_accuracy: 0.0037 - val_loss: 9.3969e-04\n",
      "Epoch 49/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 135ms/step - accuracy: 0.0000e+00 - loss: 0.0017 - val_accuracy: 0.0037 - val_loss: 9.5210e-04\n",
      "Epoch 50/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0016 - val_accuracy: 0.0037 - val_loss: 9.4756e-04\n",
      "Epoch 51/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 136ms/step - accuracy: 0.0000e+00 - loss: 0.0016 - val_accuracy: 0.0037 - val_loss: 9.0973e-04\n",
      "Epoch 52/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0018 - val_accuracy: 0.0037 - val_loss: 0.0020\n",
      "Epoch 53/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0022 - val_accuracy: 0.0037 - val_loss: 0.0012\n",
      "Epoch 54/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0017 - val_accuracy: 0.0037 - val_loss: 9.7235e-04\n",
      "Epoch 55/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0018 - val_accuracy: 0.0037 - val_loss: 8.7800e-04\n",
      "Epoch 56/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0017 - val_accuracy: 0.0037 - val_loss: 8.8034e-04\n",
      "Epoch 57/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0017 - val_accuracy: 0.0037 - val_loss: 9.7019e-04\n",
      "Epoch 58/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0016 - val_accuracy: 0.0037 - val_loss: 8.7555e-04\n",
      "Epoch 59/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 132ms/step - accuracy: 0.0000e+00 - loss: 0.0015 - val_accuracy: 0.0037 - val_loss: 8.4482e-04\n",
      "Epoch 60/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0014 - val_accuracy: 0.0037 - val_loss: 9.4468e-04\n",
      "Epoch 61/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 132ms/step - accuracy: 0.0000e+00 - loss: 0.0013 - val_accuracy: 0.0037 - val_loss: 8.2729e-04\n",
      "Epoch 62/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0014 - val_accuracy: 0.0037 - val_loss: 8.3513e-04\n",
      "Epoch 63/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 136ms/step - accuracy: 0.0000e+00 - loss: 0.0014 - val_accuracy: 0.0037 - val_loss: 9.1858e-04\n",
      "Epoch 64/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0015 - val_accuracy: 0.0037 - val_loss: 8.1060e-04\n",
      "Epoch 65/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0014 - val_accuracy: 0.0037 - val_loss: 8.9613e-04\n",
      "Epoch 66/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0014 - val_accuracy: 0.0037 - val_loss: 7.7653e-04\n",
      "Epoch 67/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 144ms/step - accuracy: 0.0000e+00 - loss: 0.0014 - val_accuracy: 0.0037 - val_loss: 7.7616e-04\n",
      "Epoch 68/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0012 - val_accuracy: 0.0037 - val_loss: 7.5345e-04\n",
      "Epoch 69/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0012 - val_accuracy: 0.0037 - val_loss: 7.3783e-04\n",
      "Epoch 70/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0014 - val_accuracy: 0.0037 - val_loss: 7.9449e-04\n",
      "Epoch 71/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0013 - val_accuracy: 0.0037 - val_loss: 7.0742e-04\n",
      "Epoch 72/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0012 - val_accuracy: 0.0037 - val_loss: 0.0013\n",
      "Epoch 73/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0014 - val_accuracy: 0.0037 - val_loss: 7.0952e-04\n",
      "Epoch 74/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0011 - val_accuracy: 0.0037 - val_loss: 6.9294e-04\n",
      "Epoch 75/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 136ms/step - accuracy: 0.0000e+00 - loss: 0.0013 - val_accuracy: 0.0037 - val_loss: 0.0016\n",
      "Epoch 76/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 132ms/step - accuracy: 0.0000e+00 - loss: 0.0014 - val_accuracy: 0.0037 - val_loss: 6.6688e-04\n",
      "Epoch 77/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0011 - val_accuracy: 0.0037 - val_loss: 8.3215e-04\n",
      "Epoch 78/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 132ms/step - accuracy: 0.0000e+00 - loss: 0.0011 - val_accuracy: 0.0037 - val_loss: 6.5968e-04\n",
      "Epoch 79/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0010 - val_accuracy: 0.0037 - val_loss: 7.4351e-04\n",
      "Epoch 80/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 132ms/step - accuracy: 0.0000e+00 - loss: 0.0011 - val_accuracy: 0.0037 - val_loss: 6.6183e-04\n",
      "Epoch 81/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 135ms/step - accuracy: 0.0000e+00 - loss: 0.0011 - val_accuracy: 0.0037 - val_loss: 6.0435e-04\n",
      "Epoch 82/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 132ms/step - accuracy: 0.0000e+00 - loss: 9.8334e-04 - val_accuracy: 0.0037 - val_loss: 6.1421e-04\n",
      "Epoch 83/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0010 - val_accuracy: 0.0037 - val_loss: 6.0501e-04\n",
      "Epoch 84/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0011 - val_accuracy: 0.0037 - val_loss: 9.4769e-04\n",
      "Epoch 85/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 132ms/step - accuracy: 0.0000e+00 - loss: 0.0011 - val_accuracy: 0.0037 - val_loss: 5.7919e-04\n",
      "Epoch 86/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 139ms/step - accuracy: 0.0000e+00 - loss: 0.0010 - val_accuracy: 0.0037 - val_loss: 5.3553e-04\n",
      "Epoch 87/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 140ms/step - accuracy: 0.0000e+00 - loss: 9.5980e-04 - val_accuracy: 0.0037 - val_loss: 0.0012\n",
      "Epoch 88/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0012 - val_accuracy: 0.0037 - val_loss: 7.5816e-04\n",
      "Epoch 89/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 132ms/step - accuracy: 0.0000e+00 - loss: 0.0011 - val_accuracy: 0.0037 - val_loss: 7.2283e-04\n",
      "Epoch 90/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 132ms/step - accuracy: 0.0000e+00 - loss: 0.0011 - val_accuracy: 0.0037 - val_loss: 5.8371e-04\n",
      "Epoch 91/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 132ms/step - accuracy: 0.0000e+00 - loss: 0.0012 - val_accuracy: 0.0037 - val_loss: 6.9405e-04\n",
      "Epoch 92/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 132ms/step - accuracy: 0.0000e+00 - loss: 0.0010 - val_accuracy: 0.0037 - val_loss: 7.8047e-04\n",
      "Epoch 93/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 8.6532e-04 - val_accuracy: 0.0037 - val_loss: 5.2233e-04\n",
      "Epoch 94/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 132ms/step - accuracy: 0.0000e+00 - loss: 9.4757e-04 - val_accuracy: 0.0037 - val_loss: 7.7171e-04\n",
      "Epoch 95/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 134ms/step - accuracy: 0.0000e+00 - loss: 0.0012 - val_accuracy: 0.0037 - val_loss: 7.2793e-04\n",
      "Epoch 96/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 0.0011 - val_accuracy: 0.0037 - val_loss: 6.4009e-04\n",
      "Epoch 97/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 8.9953e-04 - val_accuracy: 0.0037 - val_loss: 5.0436e-04\n",
      "Epoch 98/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 133ms/step - accuracy: 0.0000e+00 - loss: 8.1257e-04 - val_accuracy: 0.0037 - val_loss: 4.4951e-04\n",
      "Epoch 99/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 135ms/step - accuracy: 0.0000e+00 - loss: 8.0476e-04 - val_accuracy: 0.0037 - val_loss: 4.2184e-04\n",
      "Epoch 100/100\n",
      "\u001b[1m12/12\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m3s\u001b[0m 135ms/step - accuracy: 0.0000e+00 - loss: 8.0816e-04 - val_accuracy: 0.0037 - val_loss: 4.2042e-04\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<keras.src.callbacks.history.History at 0x7d5d7a72ab90>"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "model.fit(X_train,y_train,validation_data=(X_test,y_test),epochs=100,batch_size=64,verbose=1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "aada6a95",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:26.450280Z",
     "iopub.status.busy": "2024-07-28T04:19:26.449870Z",
     "iopub.status.idle": "2024-07-28T04:19:26.825890Z",
     "shell.execute_reply": "2024-07-28T04:19:26.824956Z"
    },
    "papermill": {
     "duration": 0.481054,
     "end_time": "2024-07-28T04:19:26.828080",
     "exception": false,
     "start_time": "2024-07-28T04:19:26.347026",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.0004204165597911924\n"
     ]
    }
   ],
   "source": [
    "test_loss = model.evaluate(X_test, y_test, verbose=0)[0]\n",
    "\n",
    "# Print the test loss\n",
    "print(\"Test loss:\", test_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "5eaff6fa",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:27.036916Z",
     "iopub.status.busy": "2024-07-28T04:19:27.036499Z",
     "iopub.status.idle": "2024-07-28T04:19:27.092433Z",
     "shell.execute_reply": "2024-07-28T04:19:27.091534Z"
    },
    "papermill": {
     "duration": 0.16307,
     "end_time": "2024-07-28T04:19:27.094900",
     "exception": false,
     "start_time": "2024-07-28T04:19:26.931830",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "\n",
    "# Save the model to a.pkl file\n",
    "with open('model.pkl', 'wb') as file:\n",
    "    pickle.dump(model, file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "fbc0a5ea",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:27.302257Z",
     "iopub.status.busy": "2024-07-28T04:19:27.301869Z",
     "iopub.status.idle": "2024-07-28T04:19:27.490538Z",
     "shell.execute_reply": "2024-07-28T04:19:27.489441Z"
    },
    "papermill": {
     "duration": 0.294633,
     "end_time": "2024-07-28T04:19:27.493020",
     "exception": false,
     "start_time": "2024-07-28T04:19:27.198387",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "import pickle\n",
    "import pandas as pd\n",
    "import numpy as np\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from keras.models import Sequential\n",
    "from keras.layers import LSTM, Dense, Input\n",
    "\n",
    "# Open the file in read-binary ('rb') mode and load the model\n",
    "with open('/kaggle/input/trained-model/model.pkl', 'rb') as file:\n",
    "    model = pickle.load(file)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "dae1280f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:27.704929Z",
     "iopub.status.busy": "2024-07-28T04:19:27.704506Z",
     "iopub.status.idle": "2024-07-28T04:19:28.920772Z",
     "shell.execute_reply": "2024-07-28T04:19:28.919588Z"
    },
    "papermill": {
     "duration": 1.324625,
     "end_time": "2024-07-28T04:19:28.923115",
     "exception": false,
     "start_time": "2024-07-28T04:19:27.598490",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test loss: 0.00168631412088871\n"
     ]
    }
   ],
   "source": [
    "test_loss = model.evaluate(X_test, y_test, verbose=0)[0]\n",
    "\n",
    "# Print the test loss\n",
    "print(\"Test loss:\", test_loss)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "5c62ec07",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:29.129442Z",
     "iopub.status.busy": "2024-07-28T04:19:29.128625Z",
     "iopub.status.idle": "2024-07-28T04:19:29.135181Z",
     "shell.execute_reply": "2024-07-28T04:19:29.134193Z"
    },
    "papermill": {
     "duration": 0.112052,
     "end_time": "2024-07-28T04:19:29.137423",
     "exception": false,
     "start_time": "2024-07-28T04:19:29.025371",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "'2.15.0'"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import tensorflow as tf\n",
    "tf.__version__"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "20b4901f",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:29.343200Z",
     "iopub.status.busy": "2024-07-28T04:19:29.342392Z",
     "iopub.status.idle": "2024-07-28T04:19:31.435165Z",
     "shell.execute_reply": "2024-07-28T04:19:31.434031Z"
    },
    "papermill": {
     "duration": 2.198151,
     "end_time": "2024-07-28T04:19:31.437637",
     "exception": false,
     "start_time": "2024-07-28T04:19:29.239486",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\u001b[1m24/24\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m2s\u001b[0m 53ms/step\n",
      "\u001b[1m9/9\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 33ms/step\n"
     ]
    }
   ],
   "source": [
    "train_predict=model.predict(X_train)\n",
    "test_predict=model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "38c07761",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:31.650355Z",
     "iopub.status.busy": "2024-07-28T04:19:31.649641Z",
     "iopub.status.idle": "2024-07-28T04:19:31.655206Z",
     "shell.execute_reply": "2024-07-28T04:19:31.654175Z"
    },
    "papermill": {
     "duration": 0.114454,
     "end_time": "2024-07-28T04:19:31.657280",
     "exception": false,
     "start_time": "2024-07-28T04:19:31.542826",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "train_predict=scaler.inverse_transform(train_predict)\n",
    "test_predict=scaler.inverse_transform(test_predict)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "816fe2f5",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:31.868847Z",
     "iopub.status.busy": "2024-07-28T04:19:31.867909Z",
     "iopub.status.idle": "2024-07-28T04:19:31.873171Z",
     "shell.execute_reply": "2024-07-28T04:19:31.872118Z"
    },
    "papermill": {
     "duration": 0.11247,
     "end_time": "2024-07-28T04:19:31.875717",
     "exception": false,
     "start_time": "2024-07-28T04:19:31.763247",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "270\n",
      "764\n"
     ]
    }
   ],
   "source": [
    "print(len(test_predict))\n",
    "print(len(train_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "845b2b94",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:32.086815Z",
     "iopub.status.busy": "2024-07-28T04:19:32.086085Z",
     "iopub.status.idle": "2024-07-28T04:19:32.188689Z",
     "shell.execute_reply": "2024-07-28T04:19:32.187596Z"
    },
    "papermill": {
     "duration": 0.209377,
     "end_time": "2024-07-28T04:19:32.191105",
     "exception": false,
     "start_time": "2024-07-28T04:19:31.981728",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "2576.6027284129473"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "import math\n",
    "from sklearn.metrics import mean_squared_error\n",
    "math.sqrt(mean_squared_error(y_train,train_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "4802329a",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:32.401050Z",
     "iopub.status.busy": "2024-07-28T04:19:32.399859Z",
     "iopub.status.idle": "2024-07-28T04:19:32.407431Z",
     "shell.execute_reply": "2024-07-28T04:19:32.406360Z"
    },
    "papermill": {
     "duration": 0.114666,
     "end_time": "2024-07-28T04:19:32.409568",
     "exception": false,
     "start_time": "2024-07-28T04:19:32.294902",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "3064.002426916406"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "math.sqrt(mean_squared_error(y_test,test_predict))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "e9ae0e6e",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:32.681920Z",
     "iopub.status.busy": "2024-07-28T04:19:32.681514Z",
     "iopub.status.idle": "2024-07-28T04:19:32.906150Z",
     "shell.execute_reply": "2024-07-28T04:19:32.905106Z"
    },
    "papermill": {
     "duration": 0.394508,
     "end_time": "2024-07-28T04:19:32.908911",
     "exception": false,
     "start_time": "2024-07-28T04:19:32.514403",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "look_back=100\n",
    "trainPredictPlot = np.empty_like(df1)\n",
    "trainPredictPlot[:, :] = np.nan\n",
    "trainPredictPlot[look_back:len(train_predict)+look_back, :] = train_predict\n",
    "# shift test predictions for plotting\n",
    "testPredictPlot = np.empty_like(df1)\n",
    "testPredictPlot[:, :] = np.nan\n",
    "testPredictPlot[len(train_predict)+(look_back*2)+1:len(df1)-1, :] = test_predict\n",
    "# plot baseline and predictions\n",
    "plt.plot(scaler.inverse_transform(df1))\n",
    "plt.plot(trainPredictPlot)\n",
    "plt.plot(testPredictPlot)\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "0bb9feaf",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:33.119533Z",
     "iopub.status.busy": "2024-07-28T04:19:33.118470Z",
     "iopub.status.idle": "2024-07-28T04:19:33.124960Z",
     "shell.execute_reply": "2024-07-28T04:19:33.123965Z"
    },
    "papermill": {
     "duration": 0.1138,
     "end_time": "2024-07-28T04:19:33.127032",
     "exception": false,
     "start_time": "2024-07-28T04:19:33.013232",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "371"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(test_data)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "b266a743",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:33.335543Z",
     "iopub.status.busy": "2024-07-28T04:19:33.334826Z",
     "iopub.status.idle": "2024-07-28T04:19:33.341936Z",
     "shell.execute_reply": "2024-07-28T04:19:33.340797Z"
    },
    "papermill": {
     "duration": 0.113487,
     "end_time": "2024-07-28T04:19:33.344065",
     "exception": false,
     "start_time": "2024-07-28T04:19:33.230578",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(1, 100)"
      ]
     },
     "execution_count": 31,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x_input=test_data[271:].reshape(1,-1)\n",
    "x_input.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "75dba5f7",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:33.554861Z",
     "iopub.status.busy": "2024-07-28T04:19:33.553966Z",
     "iopub.status.idle": "2024-07-28T04:19:33.561585Z",
     "shell.execute_reply": "2024-07-28T04:19:33.560604Z"
    },
    "papermill": {
     "duration": 0.114938,
     "end_time": "2024-07-28T04:19:33.563550",
     "exception": false,
     "start_time": "2024-07-28T04:19:33.448612",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[0.8894596879496273,\n",
       " 0.8848487778710137,\n",
       " 0.8831196046159131,\n",
       " 0.893316939802898,\n",
       " 0.8691096073889788,\n",
       " 0.8343281106754039,\n",
       " 0.8352148829833956,\n",
       " 0.7859354128517453,\n",
       " 0.7614398848207014,\n",
       " 0.7391610688833745,\n",
       " 0.7510652826426234,\n",
       " 0.7413336285162948,\n",
       " 0.7429962445062468,\n",
       " 0.7303604491997018,\n",
       " 0.733441857371012,\n",
       " 0.7250401434780573,\n",
       " 0.7310698452903808,\n",
       " 0.7533263660972229,\n",
       " 0.730471377825733,\n",
       " 0.7402472940867253,\n",
       " 0.7412006236598654,\n",
       " 0.7490923943575001,\n",
       " 0.7512204734033185,\n",
       " 0.7690213836708131,\n",
       " 0.7651862084955889,\n",
       " 0.7608414173560536,\n",
       " 0.7668488244855409,\n",
       " 0.7624818478893882,\n",
       " 0.7693981035572655,\n",
       " 0.7554545543692606,\n",
       " 0.703359904231597,\n",
       " 0.69941401888278,\n",
       " 0.6705070336684815,\n",
       " 0.682721518827605,\n",
       " 0.687088605097625,\n",
       " 0.687088605097625,\n",
       " 0.682056494545457,\n",
       " 0.6851157168125017,\n",
       " 0.7042909386742526,\n",
       " 0.7070841512284383,\n",
       " 0.7067737701546961,\n",
       " 0.699680028595639,\n",
       " 0.7139118830746363,\n",
       " 0.7029608896623083,\n",
       " 0.6936281408790499,\n",
       " 0.6797067771476624,\n",
       " 0.6840515682871977,\n",
       " 0.6823889518495971,\n",
       " 0.691411428785333,\n",
       " 0.6795072150260841,\n",
       " 0.6865343993199926,\n",
       " 0.6937390695050811,\n",
       " 0.7044905007958306,\n",
       " 0.7059091833033218,\n",
       " 0.7037590284747532,\n",
       " 0.7254613439516115,\n",
       " 0.7100102594128341,\n",
       " 0.7138009544486046,\n",
       " 0.698349870357476,\n",
       " 0.7159069568163767,\n",
       " 0.6971750116585782,\n",
       " 0.6926971064363943,\n",
       " 0.6863127609680164,\n",
       " 0.6764258068547739,\n",
       " 0.6768027451936642,\n",
       " 0.6915666195460282,\n",
       " 0.7062195648247123,\n",
       " 0.7030495231578551,\n",
       " 0.7002564198298886,\n",
       " 0.6925862874842303,\n",
       " 0.7032490852794331,\n",
       " 0.7066628415286649,\n",
       " 0.7507770870254986,\n",
       " 0.7305378258646626,\n",
       " 0.7316018651637477,\n",
       " 0.7231115173275977,\n",
       " 0.6922094583715588,\n",
       " 0.6331540719789164,\n",
       " 0.660265436346833,\n",
       " 0.7081481905275233,\n",
       " 0.7023401270671761,\n",
       " 0.6790860145525297,\n",
       " 0.6792412053132251,\n",
       " 0.6774012131059606,\n",
       " 0.6959779675030637,\n",
       " 0.7110300370249039,\n",
       " 0.7190104412180851,\n",
       " 0.7061531167857826,\n",
       " 0.7050668920300804,\n",
       " 0.7223134877413717,\n",
       " 0.7174143820456333,\n",
       " 0.7101210788126464,\n",
       " 0.7090126681526787,\n",
       " 0.7024731319236055,\n",
       " 0.6985716179356711,\n",
       " 0.7447253085351282,\n",
       " 0.7194760130525224,\n",
       " 0.7296288676524052,\n",
       " 0.7339960627009958,\n",
       " 0.7323844849001229]"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "temp_input=list(x_input)\n",
    "temp_input=temp_input[0].tolist()\n",
    "temp_input"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "e10cd0b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:33.775885Z",
     "iopub.status.busy": "2024-07-28T04:19:33.774716Z",
     "iopub.status.idle": "2024-07-28T04:19:36.527283Z",
     "shell.execute_reply": "2024-07-28T04:19:36.526022Z"
    },
    "papermill": {
     "duration": 2.860487,
     "end_time": "2024-07-28T04:19:36.529822",
     "exception": false,
     "start_time": "2024-07-28T04:19:33.669335",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[0.71461314]\n",
      "101\n",
      "1 day output [[0.7167031]]\n",
      "2 day output [[0.7184738]]\n",
      "3 day output [[0.7198869]]\n",
      "4 day output [[0.7209615]]\n",
      "5 day output [[0.7217453]]\n",
      "6 day output [[0.722297]]\n",
      "7 day output [[0.72267234]]\n",
      "8 day output [[0.72292143]]\n",
      "9 day output [[0.7230841]]\n",
      "10 day output [[0.7231907]]\n",
      "11 day output [[0.7232622]]\n",
      "12 day output [[0.72331387]]\n",
      "13 day output [[0.72335464]]\n",
      "14 day output [[0.7233899]]\n",
      "15 day output [[0.72342265]]\n",
      "16 day output [[0.7234545]]\n",
      "17 day output [[0.72348535]]\n",
      "18 day output [[0.7235155]]\n",
      "19 day output [[0.72354454]]\n",
      "20 day output [[0.72357213]]\n",
      "21 day output [[0.723598]]\n",
      "22 day output [[0.7236222]]\n",
      "23 day output [[0.7236446]]\n",
      "24 day output [[0.7236652]]\n",
      "25 day output [[0.7236843]]\n",
      "26 day output [[0.7237021]]\n",
      "27 day output [[0.72371835]]\n",
      "28 day output [[0.7237335]]\n",
      "29 day output [[0.7237476]]\n"
     ]
    }
   ],
   "source": [
    "lst_output=[]\n",
    "n_steps=100\n",
    "i=0\n",
    "while(i<30):\n",
    "    \n",
    "    if(len(temp_input)>100):\n",
    "        #print(temp_input)\n",
    "        x_input=np.array(temp_input[1:])\n",
    "#         print(\"{} day input {}\".format(i,x_input))\n",
    "        x_input=x_input.reshape(1,-1)\n",
    "        x_input = x_input.reshape((1, n_steps, 1))\n",
    "        #print(x_input)\n",
    "        yhat = model.predict(x_input, verbose=0)\n",
    "        print(\"{} day output {}\".format(i,yhat))\n",
    "        temp_input.extend(yhat[0].tolist())\n",
    "        temp_input=temp_input[1:]\n",
    "        #print(temp_input)\n",
    "        lst_output.extend(yhat.tolist())\n",
    "        i=i+1\n",
    "    else:\n",
    "        x_input = x_input.reshape((1, n_steps,1))\n",
    "        yhat = model.predict(x_input, verbose=0)\n",
    "        print(yhat[0])\n",
    "        temp_input.extend(yhat[0].tolist())\n",
    "        print(len(temp_input))\n",
    "        lst_output.extend(yhat.tolist())\n",
    "        i=i+1\n",
    "\n",
    "# print(lst_output)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 34,
   "id": "a9927df3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:36.744363Z",
     "iopub.status.busy": "2024-07-28T04:19:36.743939Z",
     "iopub.status.idle": "2024-07-28T04:19:36.748781Z",
     "shell.execute_reply": "2024-07-28T04:19:36.747817Z"
    },
    "papermill": {
     "duration": 0.114816,
     "end_time": "2024-07-28T04:19:36.750942",
     "exception": false,
     "start_time": "2024-07-28T04:19:36.636126",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "day_new=np.arange(1,101)\n",
    "day_pred=np.arange(101,131)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 35,
   "id": "dcc89185",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:36.961951Z",
     "iopub.status.busy": "2024-07-28T04:19:36.961193Z",
     "iopub.status.idle": "2024-07-28T04:19:36.968145Z",
     "shell.execute_reply": "2024-07-28T04:19:36.967109Z"
    },
    "papermill": {
     "duration": 0.114742,
     "end_time": "2024-07-28T04:19:36.970301",
     "exception": false,
     "start_time": "2024-07-28T04:19:36.855559",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1236"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(df1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 36,
   "id": "2b1774e3",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:37.182517Z",
     "iopub.status.busy": "2024-07-28T04:19:37.182146Z",
     "iopub.status.idle": "2024-07-28T04:19:37.186625Z",
     "shell.execute_reply": "2024-07-28T04:19:37.185466Z"
    },
    "papermill": {
     "duration": 0.112799,
     "end_time": "2024-07-28T04:19:37.188806",
     "exception": false,
     "start_time": "2024-07-28T04:19:37.076007",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "# print(scaler.inverse_transform(df1[1136:]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 37,
   "id": "77041cbe",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:37.401271Z",
     "iopub.status.busy": "2024-07-28T04:19:37.400878Z",
     "iopub.status.idle": "2024-07-28T04:19:37.597218Z",
     "shell.execute_reply": "2024-07-28T04:19:37.596194Z"
    },
    "papermill": {
     "duration": 0.304829,
     "end_time": "2024-07-28T04:19:37.599404",
     "exception": false,
     "start_time": "2024-07-28T04:19:37.294575",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7d5d78939930>]"
      ]
     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(day_new,scaler.inverse_transform(df1[1136:]))\n",
    "plt.plot(day_pred,scaler.inverse_transform(lst_output))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "f5f507b6",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:37.815658Z",
     "iopub.status.busy": "2024-07-28T04:19:37.815264Z",
     "iopub.status.idle": "2024-07-28T04:19:38.059988Z",
     "shell.execute_reply": "2024-07-28T04:19:38.058851Z"
    },
    "papermill": {
     "duration": 0.355696,
     "end_time": "2024-07-28T04:19:38.062271",
     "exception": false,
     "start_time": "2024-07-28T04:19:37.706575",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7d5d7acc5300>]"
      ]
     },
     "execution_count": 38,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "df3=df1.tolist()\n",
    "df3.extend(lst_output)\n",
    "plt.plot(df3[:])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "929b8bfc",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:38.277963Z",
     "iopub.status.busy": "2024-07-28T04:19:38.276908Z",
     "iopub.status.idle": "2024-07-28T04:19:38.283703Z",
     "shell.execute_reply": "2024-07-28T04:19:38.282603Z"
    },
    "papermill": {
     "duration": 0.117357,
     "end_time": "2024-07-28T04:19:38.286148",
     "exception": false,
     "start_time": "2024-07-28T04:19:38.168791",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "df3=scaler.inverse_transform(df3).tolist()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 40,
   "id": "1960c826",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:38.507937Z",
     "iopub.status.busy": "2024-07-28T04:19:38.506833Z",
     "iopub.status.idle": "2024-07-28T04:19:38.744365Z",
     "shell.execute_reply": "2024-07-28T04:19:38.743365Z"
    },
    "papermill": {
     "duration": 0.350384,
     "end_time": "2024-07-28T04:19:38.746542",
     "exception": false,
     "start_time": "2024-07-28T04:19:38.396158",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[<matplotlib.lines.Line2D at 0x7d5d7acbb160>]"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.plot(df3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "f5c47c75",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:38.965044Z",
     "iopub.status.busy": "2024-07-28T04:19:38.964221Z",
     "iopub.status.idle": "2024-07-28T04:19:38.969345Z",
     "shell.execute_reply": "2024-07-28T04:19:38.968304Z"
    },
    "papermill": {
     "duration": 0.115803,
     "end_time": "2024-07-28T04:19:38.971473",
     "exception": false,
     "start_time": "2024-07-28T04:19:38.855670",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "stock_price=scaler.inverse_transform(lst_output)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "299616db",
   "metadata": {
    "execution": {
     "iopub.execute_input": "2024-07-28T04:19:39.186628Z",
     "iopub.status.busy": "2024-07-28T04:19:39.185715Z",
     "iopub.status.idle": "2024-07-28T04:19:39.191846Z",
     "shell.execute_reply": "2024-07-28T04:19:39.190816Z"
    },
    "papermill": {
     "duration": 0.116833,
     "end_time": "2024-07-28T04:19:39.194729",
     "exception": false,
     "start_time": "2024-07-28T04:19:39.077896",
     "status": "completed"
    },
    "tags": []
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Price on Day 1 is 2866.100714124222\n",
      "Price on Day 2 is 2870.7695057119286\n",
      "Price on Day 3 is 2874.725009695958\n",
      "Price on Day 4 is 2877.881743410347\n",
      "Price on Day 5 is 2880.282315037508\n",
      "Price on Day 6 is 2882.0332450334677\n",
      "Price on Day 7 is 2883.2656867097116\n",
      "Price on Day 8 is 2884.104135849222\n",
      "Price on Day 9 is 2884.660572081398\n",
      "Price on Day 10 is 2885.0239399870234\n",
      "Price on Day 11 is 2885.262146356816\n",
      "Price on Day 12 is 2885.421793890288\n",
      "Price on Day 13 is 2885.5372354345086\n",
      "Price on Day 14 is 2885.628310424412\n",
      "Price on Day 15 is 2885.707135561873\n",
      "Price on Day 16 is 2885.7802352248223\n",
      "Price on Day 17 is 2885.851337629221\n",
      "Price on Day 18 is 2885.920309624499\n",
      "Price on Day 19 is 2885.9876838129367\n",
      "Price on Day 20 is 2886.052528140544\n",
      "Price on Day 21 is 2886.11417685447\n",
      "Price on Day 22 is 2886.171964201865\n",
      "Price on Day 23 is 2886.2260233332995\n",
      "Price on Day 24 is 2886.2760879476327\n",
      "Price on Day 25 is 2886.3220248942944\n",
      "Price on Day 26 is 2886.3647662272756\n",
      "Price on Day 27 is 2886.4044450971464\n",
      "Price on Day 28 is 2886.440795202766\n",
      "Price on Day 29 is 2886.4746154475547\n",
      "Price on Day 30 is 2886.506172132653\n"
     ]
    }
   ],
   "source": [
    "for i in range(len(stock_price)):\n",
    "    day_price=stock_price[i].item()\n",
    "    print(\"Price on Day\",i+1,\"is\",day_price)"
   ]
  }
 ],
 "metadata": {
  "kaggle": {
   "accelerator": "none",
   "dataSources": [
    {
     "datasetId": 5151839,
     "sourceId": 8609319,
     "sourceType": "datasetVersion"
    },
    {
     "datasetId": 5156884,
     "sourceId": 8615977,
     "sourceType": "datasetVersion"
    },
    {
     "datasetId": 5200166,
     "sourceId": 8675653,
     "sourceType": "datasetVersion"
    },
    {
     "datasetId": 5200225,
     "sourceId": 8675730,
     "sourceType": "datasetVersion"
    }
   ],
   "dockerImageVersionId": 30732,
   "isGpuEnabled": false,
   "isInternetEnabled": false,
   "language": "python",
   "sourceType": "notebook"
  },
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.10.13"
  },
  "papermill": {
   "default_parameters": {},
   "duration": 205.796297,
   "end_time": "2024-07-28T04:19:41.128428",
   "environment_variables": {},
   "exception": null,
   "input_path": "__notebook__.ipynb",
   "output_path": "__notebook__.ipynb",
   "parameters": {},
   "start_time": "2024-07-28T04:16:15.332131",
   "version": "2.5.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}